A Comparison of Three Anatomical Structures for Estimating Age in a Slow-Growing Subarctic Population of Lake Whitefish
Bibliographic record
Abstract
Abstract It has been well documented in previous research that otoliths are the preferred hard structure for estimating age in coregonids. However, the slower growth due to short growing seasons experienced by populations in the subarctic region of the boreal zone may alter the utility of alternative nonlethal structures for estimating age. We compared the three most commonly used age estimation structures (otoliths, pectoral fin rays, and scales) for Lake Whitefish Coregonus clupeaformis in a northern (above 50°N) population from Great Slave Lake. This study provides new perspectives regarding the use of different aging structures with fish populations typical of subarctic and arctic environments. Results of ANOVA showed that reader confidence, within-reader precision, and the age estimates themselves were all affected by age structure; reader confidence also varied with age-class. Reader confidence was highest for age estimates from otoliths, followed by pectoral fin rays and then scales. Similarly, within-reader precision (as measured by CVs) was highest for age estimates from otoliths, followed by scales and then pectoral fin ray sections. Pairwise comparisons between age estimates from otoliths and those from scales or pectoral fin rays indicated no significant differences when fish were younger than 10 years (scales) or 12 years (fin rays), suggesting that these nonlethal structures could be conservatively used to reliably estimate ages of younger (<10 years) and smaller (≤300 mm FL) Lake Whitefish. Of particular significance are the findings that (1) divergence between scale and otolith age estimates is delayed by 5–6 years relative to more southerly populations; and (2) in contrast to examples from southern populations, fin rays do not offer a suitable nonlethal alternative for estimating ages of older Lake Whitefish (>11 years). Received March 4, 2014; accepted December 3, 2014
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".